Computational investigation of influences of anxiety and depression on human decision making under uncertainty.
Sonia Bishop, Professor
Psychology
Closed. This professor is continuing with Fall 2024 apprentices on this project; no new apprentices needed for Spring 2025.
Computational models have been a powerful tool for studying decision-making in both psychology and neuroscience. They have recently become popular in psychiatry as well. Part of the appeal has been that computational approaches delineate individual differences in decision-making that can explain why people with different psychiatric disorders (such as anxiety or depression) often make poor decisions.
Our lab investigate potential decision-making biases exhibited by anxious and depressed individuals. To do so, we leverage behavioral experiments, fMRI, and computational models inspired by Bayesian statistics and reinforcement learning algorithms from AI.
Specific to this project:
We have several ongoing projects involving computational modeling of decision-making as relates to psychopathology.
Role: To assist with data analysis and computational modeling of behavioral data. May be done partly online and/or partly in person at Berkeley Way West (flexible based on student). Learning outcome will be to understand basic principles of computational modeling of behavior in psychopathology.
Qualifications: Coding skills (ideally in Python, but R or matlab may also be useful) are needed for this role. Should have an ability to convey information clearly verbally and in writing. Psych, CogSci, or Computer Science major or minor.
Day-to-day supervisor for this project: Jennifer Senta, Ph.D. candidate
Hours: 9-11 hrs
Education, Cognition & Psychology Social Sciences